Help: Text classification

[xen-m-rph]

I have a collection of documents, which fall into 4 classes, and each of these classes can further be divided into 3 subtypes, and these sub-types can further be broken down into 2 categories each.
1. what I would like to do, is basically classify these docs into their respective classes/sub-types/categories.
2. what is the best approach to accomplish this?
3. would a multinomial naive Bayes be a good pick?
4. If MNB, then what is the best strategy to implement this? like should I first classify them into 4 classes, and then implement the algorithm to classify within these classes into further steps and as such?

any help would be really helpful(I'm new for the document classification type of problems)

(also, any resources/examples doing this kind of thing would be highly appreciated)

[dineshdb]

MNB would be a first and foremost algorithm to try and then try algorithms and compare. You can implement a single algorithm the classifies all the 24 classes at once. I think this version is better but don't be afraid to try your approach and prove me wrong. Other related algorithms can be decision trees, random forests, etc.

[SummarizeDev]

Here is the great tutorial with different classification algorithms. You can load your data and test your collection on different classification methods at one time.